1. Technical Field
Embodiments of the subject matter disclosed herein generally relate to methods used for processing seismic data, and more particularly, to a hybrid deblending of seismic data using two different deblending techniques, for example, modeling denoising and impulsive denoising.
2. Discussion of the Background
Simultaneous source acquisition, which is characterized by an interval between shots (i.e., source activations to generate waves incident on an explored underground formation) shorter than listening time necessary to record energy due to one shot, is a desirable manner of acquiring seismic data because it reduces a survey's total acquisition time and cost, or it may be used to acquire a higher density dataset in the same survey time. Simultaneous source acquisition can be performed on land and in marine environments (with ocean bottom receivers or towed streamers), with continuous or non-continuous recording. Using simultaneous source acquisition yields blended data (i.e., including overlapping signals) and therefore requires additional pre-processing to extract seismic datasets focusing on individual signals, which is known as “deblending.”
In conventional surveying techniques, sources are activated so a signal corresponding to one shot does not overlap another signal corresponding to another shot in their significant portions (e.g., when the ratio of the signals' amplitudes is substantially larger than each of the individual signal-to-noise ratios). FIG. 1A illustrates seismic waves generated at different spatial positions 10, 12 and 14 at intervals so recorded wavelets 10a-c corresponding to the seismic waves generated at spatial position 10 do not interfere with wavelets 12a-c corresponding to the seismic waves generated at spatial position 12. The wavelets generated due to one shot form a signal carrying information about the explored underground structure.
The receivers may record continuously in time (i.e., 16 in FIG. 1A) or separately to form regular seismic traces for each individual shot, as shown in FIG. 1B. The traces illustrated in FIG. 1B form a receiver gather 20. First wavelets, which correspond to reflections from a first interface, form curve a, second wavelets form curve b, etc.
FIG. 2A illustrates seismic waves generated at the same positions as in FIG. 1A, but at shorter intervals so the corresponding recording times partially overlap. Therefore, for example, wavelet 10c overlaps wavelet 12a. FIG. 2B shows receiver gather 30 formed with regular seismic traces extracted from continuous recording based on each shot's start time. FIG. 2B data has been acquired in less time than FIG. 1B data. Cross-talk such as 32, which appears to be noise on the traces, is in fact another trace's signal wavelet. When simultaneous source acquisition is used, it is necessary to separate (deblend) the energy (wavelets) associated with each shot as a pre-processing step.
In land simultaneous source acquisition, a variety of different sources (for example, different vibroseis sweeps or pseudo-random sweeps) yielding different signatures are used to ease separation of blended data. When energy from a given shot is time-aligned, a source designature operator for that shot can be applied to focus the energy related to that shot while keeping energy from other shots dispersed.
In marine acquisition, randomness of firing time of the sources (as described in the article, “A Universal Simultaneous Shooting Technique by DeKok et al., EAGE 64th Conference & Exhibition 2002, pp. 1-4, the entire content of which is incorporated herein by reference) may be used for deblending the data.
Varying shot timing (known as “timing dither”), which is seismic source activations at varying intervals, yields incoherency in cross-talk noise timing in all domains other than the shot domain. For example, FIG. 3 (corresponding to Hampson et al., “Acquisition using simultaneous sources”, Leading Edge, Vol. 27, No. 7, the entire content of which is incorporated herein by reference) is a sequence of graphs representing the same blended seismic data in different domains: common shot, common receiver, common midpoint, and common offset.
Deblending techniques may be characterized into a number of classes. A first class attenuates blending noise by impulsive denoising, a second class uses a model (including using an inversion) of the data to perform deblending, a third class where deblending is achieved through joint modelling, or other methods (as described, for example, in “A deblending strategy using alternating constant delay simultaneous source data,” by Poole et al., SEG 2014 conference and proceedings, the entire content of which is incorporated herein by reference).
Impulsive denoising techniques (disclosed, for example, in the article, “Acquisition using simultaneous sources,” by Stefani et al., published in 69th EAGE Conference & Exhibition, 2007, the entire content of which is incorporated herein by reference) use the fact that when data is sorted into any domain other than the common shot, cross-talk noise from other sources is incoherent, as illustrated in FIG. 3 (corresponding to the previously referred-to article of Hampson et al.). Note that in the common shot domain, cross-talk noise 40 is continuous. While these techniques may effectively remove the strongest cross-talk energy, low-amplitude cross-talk noise is not seen as impulsive and is not removed. Additionally these techniques are prone to signal damage.
Modeling and subtraction techniques are appealing when applied to a problem that is well-defined, but may fail when the cross-talk noise is too complex to be modeled.
Separation in a model domain may be used when the energy coming from different sources can be separated through muting in a model domain. For example, one such method (described in the article, “Fast and robust deblending using Apex Shifted Radon transform,” by Trad et al., published in SEG Expanded Abstracts 2012, the entire content of which is incorporated herein by reference) uses an apex-shifted Radon to separate cross-talk noise.
Iterative coherency enhancement/denoising techniques (described, for example, in the article, “Separating simultaneous sources by inversion,” by Abma et al., published in 71st EAGE Conference & Exhibition, 2009; the article, “Source Separation by Iterative Rank Reduction—Theory and Applications,” by M. Maraschini et al., published in 74th EAGE Conference & Exhibition, 2012; and the article, “An iterative SVD method for deblending: theory and examples,” by M. Maraschini et al., published in SEG Technical Program Expanded Abstracts 2012, the entire contents of which are incorporated herein by reference) rely on the fact that cross-talk noise on some traces is a duplication of signal on other traces. This means that with knowledge of the timing of all shots, a signal estimate made for one source can then be used to reduce the level of cross-talk for all other sources.
The full modeling of energy from all sources technique (described, for example, in the article, “Simultaneous source separation by sparse Radon transform,” by Akerberg et al., published in 78th Annual International SEG Meeting, 2008; and the article, “Simultaneous source separation using dithered sources,” by Moore et al., published in 78th Annual International SEG Meeting, 2008, the entire contents of which are incorporated herein by reference) has similarities to the iterative denoising method, except that this formulation solves the relationship between source energy and cross-talk noise implicitly at the core of the problem formulation. Equations can be formulated as designing a transform domain for each source or spatial area (e.g., tau-p domain, Fourier domain, etc.) such that when it is reverse-transformed and reblended, the raw input data is reconstructed as accurately as possible in a least squares sense.
This technique (i.e., full modeling of energy from all sources) uses the timing and positioning of all sources and also relies on a sparse solution to the equations. Once the transform domains have been calculated, the final step to deblend the data requires application of reverse-transform without reblending. While this method may result in some filtering of the original data, it removes low-amplitude cross-talk noise and preserves the primary signal. This method could be considered an alternative way of solving the same problem as the iterative coherency enhancement/denoising technique (analogous to sparse least squares Radon versus inversion through “iterative cleaning”).
It is, however, desirable to develop deblending methods able to use the first and second class of deblending methods' strengths, while avoiding their pitfalls.